
'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCAmelCase					:    Optional[int]           = r"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `\" / \"`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `\" // \"`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `\"train\"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `\"compressed\"`)
            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
            `\"compressed\"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
"""


@add_start_docstrings(a_)
class 			SCREAMING_SNAKE_CASE__      (     a_):
				lowerCAmelCase_     			=							'''rag'''
				lowerCAmelCase_     			=							True







				def __init__(       self						,				A_=None						,				A_=True						,				A_=None						,				A_=None						,				A_=None						,				A_=None						,				A_=None						,				A_=" / "						,				A_=" // "						,				A_=5						,				A_=300						,				A_=768						,				A_=8						,				A_="wiki_dpr"						,				A_="train"						,				A_="compressed"						,				A_=None						,				A_=None						,				A_=False						,				A_=False						,				A_=0.0						,				A_=True						,				A_=False						,				A_=False						,				A_=False						,				A_=True						,				A_=None						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(
											    bos_token_id=_lowercase						,				pad_token_id=_lowercase						,				eos_token_id=_lowercase						,				decoder_start_token_id=_lowercase						,				forced_eos_token_id=_lowercase						,				is_encoder_decoder=_lowercase						,				prefix=_lowercase						,				vocab_size=_lowercase						,				**_lowercase						,				)
											assert (
											    "question_encoder" in kwargs and "generator" in kwargs
											), "Config has to be initialized with question_encoder and generator config"
											UpperCamelCase   			=     kwargs.pop('question_encoder'     )
											UpperCamelCase   			=     question_encoder_config.pop('model_type'     )
											UpperCamelCase   			=     kwargs.pop('generator'     )
											UpperCamelCase   			=     decoder_config.pop('model_type'     )

											from ..auto.configuration_auto import AutoConfig

											UpperCamelCase   			=     AutoConfig.for_model(_lowercase						,				**_lowercase     )
											UpperCamelCase   			=     AutoConfig.for_model(_lowercase						,				**_lowercase     )

											UpperCamelCase   			=     reduce_loss
											UpperCamelCase   			=     label_smoothing
											UpperCamelCase   			=     exclude_bos_score
											UpperCamelCase   			=     do_marginalize

											UpperCamelCase   			=     title_sep
											UpperCamelCase   			=     doc_sep
											UpperCamelCase   			=     n_docs
											UpperCamelCase   			=     max_combined_length

											UpperCamelCase   			=     dataset
											UpperCamelCase   			=     dataset_split
											UpperCamelCase   			=     index_name

											UpperCamelCase   			=     retrieval_vector_size
											UpperCamelCase   			=     retrieval_batch_size
											UpperCamelCase   			=     passages_path
											UpperCamelCase   			=     index_path
											UpperCamelCase   			=     use_dummy_dataset

											UpperCamelCase   			=     output_retrieved

											UpperCamelCase   			=     do_deduplication

											UpperCamelCase   			=     use_cache

											if self.forced_eos_token_id is None:
																		UpperCamelCase   			=     getattr(self.generator						,				'forced_eos_token_id'						,				_lowercase     )







				@classmethod
				def 						UpperCAmelCase_    (       cls						,				A_						,				A_						,				**A_     )->		PretrainedConfig:







											'''simple docstring'''






											return cls(question_encoder=question_encoder_config.to_dict()						,				generator=generator_config.to_dict()						,				**_lowercase     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     copy.deepcopy(self.__dict__     )
											UpperCamelCase   			=     self.question_encoder.to_dict()
											UpperCamelCase   			=     self.generator.to_dict()
											UpperCamelCase   			=     self.__class__.model_type
											return output







from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


